mlsanityData to 7 Oct 2026

Searching for Structure in Unfalsifiable Claims

arXiv 2209.00495 ↗First posted 19 Aug 2022cs.CL cs.LG cs.SIcited by 4

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Abstract (arXiv)

Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfalsifiable nature makes them ill-suited to fact-checking pipelines. In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic. Understanding and visualizing these narratives can facilitate more informed debates on social media. As a first step towards systematically identifying the underlying narratives on social media, we introduce PAPYER, a fine-grained dataset of online comments related to hygiene in public restrooms, which contains a multitude of unfalsifiable claims. We present a human-in-the-loop pipeline that uses a combination of machine and human kernels to discover the prevailing narratives and show that this pipeline outperforms recent large transformer models and state-of-the-art unsupervised topic models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 19 Aug 2022.

Builds on

24 of 58 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  7. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  8. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  9. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  10. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  11. 2020Social Chemistry 101: Learning to Reason about Social and Moral Normscited by 253
  12. 2020Stance Detection on Social Media: State of the Art and Trendscited by 93
12 more
  1. 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
  2. 2020Topic Modeling with Contextualized Word Representation Clusterscited by 41
  3. 2019Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learningcited by 32
  4. 2018ClaimRank: Detecting Check-Worthy Claims in Arabic and Englishcited by 27
  5. 2020Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approachcited by 23
  6. 2020Intentonomy: a Dataset and Study towards Human Intent Understandingcited by 22
  7. 2016Mixing Dirichlet Topic Models and Word Embeddings to Make lda2veccited by 19
  8. 2014Cost-Effective HITs for Relative Similarity Comparisonscited by 13
  9. 2021Exploring Visual Engagement Signals for Representation Learningcited by 12
  10. 2021Towards Explainable Fact Checkingcited by 9
  11. 2015Learning Concept Embeddings with Combined Human-Machine Expertisecited by 6
  12. 2015Topic segmentation via community detection in complex networkscited by 3

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Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

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  3. 2023Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learningcited by 2
  4. 2025Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memescited by 2